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<div class="title">time_averaged_method.hpp</div>  </div>
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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;</div><div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="preprocessor">#pragma once</span></div><div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;</div><div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="time__averaged__method_8h.html">time_averaged_method.h</a>&quot;</span></div><div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;</div><div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160; <span class="comment">//=================================================================================================//</span></div><div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespace_s_p_h.html">SPH</a></div><div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;{</div><div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;    <span class="comment">//=================================================================================================//</span></div><div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;    <span class="keyword">template</span>&lt;<span class="keyword">class</span> ObserveMethodType&gt;</div><div class="line"><a name="l00015"></a><span class="lineno"><a class="line" href="class_s_p_h_1_1_regression_test_time_averaged.html#a400f76d3203ec1f7d6c1fd4caa1329de">   15</a></span>&#160;    <span class="keywordtype">void</span> <a class="code" href="class_s_p_h_1_1_regression_test_time_averaged.html#a400f76d3203ec1f7d6c1fd4caa1329de">RegressionTestTimeAveraged&lt;ObserveMethodType&gt;::filterLocalResult</a>(DoubleVec&lt;Real&gt; &amp;current_result)</div><div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;    {</div><div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;        <span class="keywordtype">int</span> scale = round(this-&gt;snapshot_ / 200);</div><div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;        std::cout &lt;&lt; <span class="stringliteral">&quot;The filter scale is &quot;</span> &lt;&lt; scale * 2 &lt;&lt; <span class="stringliteral">&quot;.&quot;</span> &lt;&lt; endl;</div><div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> snapshot_index = 0; snapshot_index != this-&gt;snapshot_; ++snapshot_index)</div><div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;        {</div><div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;            <span class="keywordflow">for</span> (<span class="keywordtype">int</span> observation_index = 0; observation_index != this-&gt;observation_; ++observation_index)</div><div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;            {</div><div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;                Real filter_meanvalue = 0;</div><div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;                Real filter_variance = 0;</div><div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160;                <span class="keywordflow">for</span> (<span class="keywordtype">int</span> index = SMAX(snapshot_index - scale, 0); index != SMIN(snapshot_index + scale, this-&gt;snapshot_); ++index)</div><div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;                {</div><div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;                    filter_meanvalue += current_result[index][observation_index];</div><div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;                }</div><div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;                filter_meanvalue = (filter_meanvalue - current_result[snapshot_index][observation_index]) / (SMIN(snapshot_index + scale, this-&gt;snapshot_) - SMAX(snapshot_index - scale, 0));</div><div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;                <span class="keywordflow">for</span> (<span class="keywordtype">int</span> index = SMAX(snapshot_index - scale, 0); index != SMIN(snapshot_index + scale, this-&gt;snapshot_); ++index)</div><div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;                {</div><div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;                    filter_variance += std::pow(current_result[index][observation_index] - filter_meanvalue, 2);</div><div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;                }</div><div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;                Real current_variance = std::pow(current_result[snapshot_index][observation_index] - filter_meanvalue, 2);</div><div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;                filter_variance = (filter_variance - current_variance) / (SMIN(snapshot_index + scale, this-&gt;snapshot_) - SMAX(snapshot_index - scale, 0));</div><div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;                <span class="keywordflow">if</span> (current_variance &gt; 4 * filter_variance)</div><div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;                {</div><div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;                    current_result[snapshot_index][observation_index] = filter_meanvalue;</div><div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;                    std::cout &lt;&lt; <span class="stringliteral">&quot;The current value of &quot;</span> &lt;&lt; this-&gt;quantity_name_ &lt;&lt; <span class="stringliteral">&quot;[&quot;</span> &lt;&lt; snapshot_index &lt;&lt; <span class="stringliteral">&quot;][&quot;</span> &lt;&lt; observation_index &lt;&lt; <span class="stringliteral">&quot;] is &quot;</span> &lt;&lt; current_result[snapshot_index][observation_index]</div><div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;                        &lt;&lt; <span class="stringliteral">&quot;, but the neighbor averaged value is &quot;</span> &lt;&lt; filter_meanvalue &lt;&lt; <span class="stringliteral">&quot;, and the rate is &quot;</span> &lt;&lt; current_variance / filter_variance &lt;&lt; endl;</div><div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;                }</div><div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;            }</div><div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;        }</div><div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;    };</div><div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;    <span class="comment">//=================================================================================================//</span></div><div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;    <span class="keyword">template</span>&lt;<span class="keyword">class</span> ObserveMethodType&gt;</div><div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;    <span class="keywordtype">void</span> <a class="code" href="class_s_p_h_1_1_regression_test_time_averaged.html#a400f76d3203ec1f7d6c1fd4caa1329de">RegressionTestTimeAveraged&lt;ObserveMethodType&gt;::filterLocalResult</a>(DoubleVec&lt;Vecd&gt; &amp;current_result)</div><div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;    {</div><div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;        <span class="keywordtype">int</span> scale = round(this-&gt;snapshot_ / 200);</div><div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;        std::cout &lt;&lt; <span class="stringliteral">&quot;The filter scale is &quot;</span> &lt;&lt; scale * 2 &lt;&lt; <span class="stringliteral">&quot;.&quot;</span> &lt;&lt; endl;</div><div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> snapshot_index = 0; snapshot_index != this-&gt;snapshot_; ++snapshot_index)</div><div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;        {</div><div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;            <span class="keywordflow">for</span> (<span class="keywordtype">int</span> observation_index = 0; observation_index != this-&gt;observation_; ++observation_index)</div><div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;            {</div><div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;                <span class="keywordflow">for</span> (<span class="keywordtype">int</span> dimension_index = 0; dimension_index != current_result[0][0].size(); ++dimension_index)</div><div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;                {</div><div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;                    Real filter_meanvalue = 0;</div><div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;                    Real filter_variance = 0;</div><div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;                    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> index = SMAX(snapshot_index - scale, 0); index != SMIN(snapshot_index + scale, this-&gt;snapshot_); ++index)</div><div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;                    {</div><div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;                        filter_meanvalue += current_result[index][observation_index][dimension_index];</div><div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;                    }</div><div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;                    filter_meanvalue = (filter_meanvalue - current_result[snapshot_index][observation_index][dimension_index]) / (SMIN(snapshot_index + scale, this-&gt;snapshot_) - SMAX(snapshot_index - scale, 0));</div><div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;                    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> index = SMAX(snapshot_index - scale, 0); index != SMIN(snapshot_index + scale, this-&gt;snapshot_); ++index)</div><div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;                    {</div><div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;                        filter_variance += std::pow(current_result[index][observation_index][dimension_index] - filter_meanvalue, 2);</div><div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;                    }</div><div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;                    Real current_variance = std::pow(current_result[snapshot_index][observation_index][dimension_index] - filter_meanvalue, 2);</div><div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;                    filter_variance = (filter_variance - current_variance) / (SMIN(snapshot_index + scale, this-&gt;snapshot_) - SMAX(snapshot_index - scale, 0));</div><div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;                    <span class="keywordflow">if</span> (current_variance &gt; 4 * filter_variance)</div><div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;                    {</div><div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;                        current_result[snapshot_index][observation_index][dimension_index] = filter_meanvalue;</div><div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;                        std::cout &lt;&lt; <span class="stringliteral">&quot;The current value of &quot;</span> &lt;&lt; this-&gt;quantity_name_ &lt;&lt; <span class="stringliteral">&quot;[&quot;</span> &lt;&lt; snapshot_index &lt;&lt; <span class="stringliteral">&quot;][&quot;</span> &lt;&lt; observation_index &lt;&lt; <span class="stringliteral">&quot;][&quot;</span> &lt;&lt; dimension_index &lt;&lt; <span class="stringliteral">&quot;] is &quot;</span> &lt;&lt; current_result[snapshot_index][observation_index][dimension_index]</div><div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;                            &lt;&lt; <span class="stringliteral">&quot;, but the neighbor averaged value is &quot;</span> &lt;&lt; filter_meanvalue &lt;&lt; <span class="stringliteral">&quot;, and the rate is &quot;</span> &lt;&lt; current_variance / filter_variance &lt;&lt; endl;</div><div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;                    }</div><div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;                }</div><div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;            }</div><div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;        }</div><div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;    };</div><div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;    <span class="comment">//=================================================================================================//</span></div><div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;    <span class="keyword">template</span>&lt;<span class="keyword">class</span> ObserveMethodType&gt;</div><div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;    <span class="keywordtype">void</span> <a class="code" href="class_s_p_h_1_1_regression_test_time_averaged.html#a400f76d3203ec1f7d6c1fd4caa1329de">RegressionTestTimeAveraged&lt;ObserveMethodType&gt;::filterLocalResult</a>(DoubleVec&lt;Matd&gt; &amp;current_result)</div><div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;    {</div><div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;        <span class="keywordtype">int</span> scale = round(this-&gt;snapshot_ / 200);</div><div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;        std::cout &lt;&lt; <span class="stringliteral">&quot;The filter scale is &quot;</span> &lt;&lt; scale * 2 &lt;&lt; <span class="stringliteral">&quot;.&quot;</span> &lt;&lt; endl;</div><div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> snapshot_index = 0; snapshot_index != this-&gt;snapshot_; ++snapshot_index)</div><div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;        {</div><div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;            <span class="keywordflow">for</span> (<span class="keywordtype">int</span> observation_index = 0; observation_index != this-&gt;observation_; ++observation_index)</div><div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;            {</div><div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;                <span class="keywordflow">for</span> (<span class="keywordtype">int</span> dimension_index_i = 0; dimension_index_i != current_result[0][0].size(); ++dimension_index_i)</div><div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;                {</div><div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;                    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> dimension_index_j = 0; dimension_index_j != current_result[0][0].size(); ++dimension_index_j)</div><div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;                    {</div><div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;                        Real filter_meanvalue = 0;</div><div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;                        Real filter_variance = 0;</div><div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;                        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> index = SMAX(snapshot_index - scale, 0); index != SMIN(snapshot_index + scale, this-&gt;snapshot_); ++index)</div><div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;                        {</div><div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;                            filter_meanvalue += current_result[index][observation_index][dimension_index_i][dimension_index_j];</div><div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;                        }</div><div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;                        filter_meanvalue = (filter_meanvalue - current_result[snapshot_index][observation_index][dimension_index_i][dimension_index_j]) / (SMIN(snapshot_index + scale, this-&gt;snapshot_) - SMAX(snapshot_index - scale, 0));</div><div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;                        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> index = SMAX(snapshot_index - scale, 0); index != SMIN(snapshot_index + scale, this-&gt;snapshot_); ++index)</div><div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;                        {</div><div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;                            filter_variance += std::pow(current_result[index][observation_index][dimension_index_i][dimension_index_j] - filter_meanvalue, 2);</div><div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;                        }</div><div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;                        Real current_variance = std::pow(current_result[snapshot_index][observation_index][dimension_index_i][dimension_index_j] - filter_meanvalue, 2);</div><div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;                        filter_variance = (filter_variance - current_variance) / (SMIN(snapshot_index + scale, this-&gt;snapshot_) - SMAX(snapshot_index - scale, 0));</div><div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;                        <span class="keywordflow">if</span> (current_variance &gt; 4 * filter_variance)</div><div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;                        {</div><div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;                            current_result[snapshot_index][observation_index][dimension_index_i][dimension_index_j] = filter_meanvalue;</div><div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;                            std::cout &lt;&lt; <span class="stringliteral">&quot;The current value of &quot;</span> &lt;&lt; this-&gt;quantity_name_ &lt;&lt; <span class="stringliteral">&quot;[&quot;</span> &lt;&lt; snapshot_index &lt;&lt; <span class="stringliteral">&quot;][&quot;</span> &lt;&lt; observation_index &lt;&lt; <span class="stringliteral">&quot;][&quot;</span> &lt;&lt; dimension_index_i &lt;&lt; <span class="stringliteral">&quot;][&quot;</span> &lt;&lt; dimension_index_j &lt;&lt; <span class="stringliteral">&quot;] is &quot;</span> &lt;&lt; current_result[snapshot_index][observation_index][dimension_index_i][dimension_index_j]</div><div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;                                &lt;&lt; <span class="stringliteral">&quot;, but the neighbor averaged value is &quot;</span> &lt;&lt; filter_meanvalue &lt;&lt; <span class="stringliteral">&quot;, and the rate is &quot;</span> &lt;&lt; current_variance / filter_variance &lt;&lt; endl;</div><div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;                        }</div><div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;                    }</div><div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;                }</div><div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;            }   </div><div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;        }</div><div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;    };</div><div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;    <span class="comment">//=================================================================================================//</span></div><div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;    <span class="keyword">template</span>&lt;<span class="keyword">class</span> ObserveMethodType&gt;</div><div class="line"><a name="l00120"></a><span class="lineno"><a class="line" href="class_s_p_h_1_1_regression_test_time_averaged.html#a15897128c44c33ecffc2bec85fb4ccc3">  120</a></span>&#160;    <span class="keywordtype">void</span> <a class="code" href="class_s_p_h_1_1_regression_test_time_averaged.html#a15897128c44c33ecffc2bec85fb4ccc3">RegressionTestTimeAveraged&lt;ObserveMethodType&gt;::searchSteadyStart</a>(DoubleVec&lt;Real&gt; &amp;current_result)</div><div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;    {</div><div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;        <span class="comment">/* the search is only for one value. */</span></div><div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;        <span class="keywordtype">int</span> scale = round(this-&gt;snapshot_ / 20);</div><div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> observation_index = 0; observation_index != this-&gt;observation_; ++observation_index)</div><div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;            <span class="keywordflow">for</span> (<span class="keywordtype">int</span> snapshot_index = this-&gt;snapshot_ - 1; snapshot_index != 3 * scale; --snapshot_index)</div><div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;            {</div><div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;                Real value_one = 0, value_two = 0;</div><div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;                <span class="keywordflow">for</span> (<span class="keywordtype">int</span> index = snapshot_index; index != snapshot_index - scale; --index)</div><div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;                {</div><div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;                    value_one += current_result[index][observation_index] / scale;</div><div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;                    value_two += current_result[index - 2 * scale][observation_index] / scale;</div><div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;                }</div><div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;</div><div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;                <span class="keywordflow">if</span> (ABS(value_one - value_two) / ABS((value_one + value_two) / 2) &gt; 0.1)</div><div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;                {</div><div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;                    snapshot_for_converged_ = SMAX(snapshot_for_converged_, snapshot_index - scale);</div><div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;                    <span class="keywordflow">break</span>;</div><div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;                }</div><div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;            }</div><div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;        std::cout &lt;&lt; <span class="stringliteral">&quot;The scale is &quot;</span> &lt;&lt; scale &lt;&lt; <span class="stringliteral">&quot;.&quot;</span> &lt;&lt; endl;</div><div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;    };</div><div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;    <span class="comment">//=================================================================================================// </span></div><div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;    <span class="keyword">template</span>&lt;<span class="keyword">class</span> ObserveMethodType&gt;</div><div class="line"><a name="l00144"></a><span class="lineno"><a class="line" href="class_s_p_h_1_1_regression_test_time_averaged.html#ad0094212252ae0e8a2ce9b658cb48f25">  144</a></span>&#160;    <span class="keywordtype">void</span> <a class="code" href="class_s_p_h_1_1_regression_test_time_averaged.html#a15897128c44c33ecffc2bec85fb4ccc3">RegressionTestTimeAveraged&lt;ObserveMethodType&gt;::searchSteadyStart</a>(DoubleVec&lt;Vecd&gt; &amp;current_result)</div><div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;    {</div><div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;        <span class="comment">/* the search is for each value within parameters. */</span></div><div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;        <span class="keywordtype">int</span> scale = round(this-&gt;snapshot_ / 20);</div><div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> observation_index = 0; observation_index != this-&gt;observation_; ++observation_index)</div><div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;            <span class="keywordflow">for</span> (<span class="keywordtype">int</span> snapshot_index = this-&gt;snapshot_ - 1; snapshot_index != 3 * scale; --snapshot_index)</div><div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;            {</div><div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;                Real value_one = 0, value_two = 0;</div><div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;                <span class="keywordflow">for</span> (<span class="keywordtype">int</span> index = snapshot_index; index != snapshot_index - scale; --index)</div><div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;                {</div><div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;                    value_one += current_result[index][observation_index][0] / scale;</div><div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;                    value_two += current_result[index - 2 * scale][observation_index][0] / scale;</div><div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;                }</div><div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;</div><div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;                <span class="keywordflow">if</span> (ABS(value_one - value_two) / ABS((value_one + value_two) / 2) &gt; 0.1)</div><div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;                {</div><div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;                    snapshot_for_converged_ = SMAX(snapshot_for_converged_, snapshot_index - scale);</div><div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;                    <span class="keywordflow">break</span>; </div><div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;                }</div><div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;            }</div><div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;        std::cout &lt;&lt; <span class="stringliteral">&quot;The scale is &quot;</span> &lt;&lt; scale &lt;&lt; <span class="stringliteral">&quot;.&quot;</span> &lt;&lt; endl;</div><div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;    };</div><div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;    <span class="comment">//=================================================================================================// </span></div><div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;    <span class="keyword">template</span>&lt;<span class="keyword">class</span> ObserveMethodType&gt;</div><div class="line"><a name="l00168"></a><span class="lineno"><a class="line" href="class_s_p_h_1_1_regression_test_time_averaged.html#afae095c5ea4c8857dd96e21a86fd9805">  168</a></span>&#160;    <span class="keywordtype">void</span> <a class="code" href="class_s_p_h_1_1_regression_test_time_averaged.html#a15897128c44c33ecffc2bec85fb4ccc3">RegressionTestTimeAveraged&lt;ObserveMethodType&gt;::searchSteadyStart</a>(DoubleVec&lt;Matd&gt; &amp;current_result)</div><div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;    {</div><div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;        <span class="keywordtype">int</span> scale = round(this-&gt;snapshot_ / 20);</div><div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> observation_index = 0; observation_index != this-&gt;observation_; ++observation_index)</div><div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;            <span class="keywordflow">for</span> (<span class="keywordtype">int</span> snapshot_index = this-&gt;snapshot_ - 1; snapshot_index != 3 * scale; --snapshot_index)</div><div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;            {</div><div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;                Real value_one = 0, value_two = 0;</div><div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;                <span class="keywordflow">for</span> (<span class="keywordtype">int</span> index = snapshot_index; index != snapshot_index - scale; --index)</div><div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;                {</div><div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;                    value_one += current_result[index][observation_index][0][0];</div><div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;                    value_two += current_result[index - 2 * scale][observation_index][0][0];</div><div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;                }</div><div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;</div><div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;                <span class="keywordflow">if</span> (ABS(value_one - value_two) / ABS((value_one + value_two) / 2) &gt; 0.1)</div><div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;                {</div><div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;                    snapshot_for_converged_ = SMAX(snapshot_for_converged_, snapshot_index - scale);</div><div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;                    <span class="keywordflow">break</span>; </div><div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;                }</div><div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;            }</div><div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;        std::cout &lt;&lt; <span class="stringliteral">&quot;The scale is &quot;</span> &lt;&lt; scale &lt;&lt; <span class="stringliteral">&quot;.&quot;</span> &lt;&lt; endl;</div><div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;    };</div><div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;    <span class="comment">//=================================================================================================//</span></div><div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;    <span class="keyword">template</span>&lt;<span class="keyword">class</span> ObserveMethodType&gt;</div><div class="line"><a name="l00191"></a><span class="lineno"><a class="line" href="class_s_p_h_1_1_regression_test_time_averaged.html#a9e897ed946fa84afe6c2624a5b46f80b">  191</a></span>&#160;    <span class="keywordtype">void</span> <a class="code" href="class_s_p_h_1_1_regression_test_time_averaged.html#a9e897ed946fa84afe6c2624a5b46f80b">RegressionTestTimeAveraged&lt;ObserveMethodType&gt;::calculateNewVariance</a>(DoubleVec&lt;Real&gt; &amp;current_result, </div><div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;        <a class="code" href="class_std_vec_3_01_real_01_4.html">StdVec&lt;Real&gt;</a> &amp;local_meanvalue, <a class="code" href="class_std_vec_3_01_real_01_4.html">StdVec&lt;Real&gt;</a> &amp;variance, <a class="code" href="class_std_vec_3_01_real_01_4.html">StdVec&lt;Real&gt;</a> &amp;variance_new)</div><div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;    {</div><div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> observation_index = 0; observation_index != this-&gt;observation_; ++observation_index)</div><div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;        {</div><div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;            <span class="keywordflow">for</span> (<span class="keywordtype">int</span> snapshot_index = snapshot_for_converged_; snapshot_index != this-&gt;snapshot_; ++snapshot_index)</div><div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;                variance_new[observation_index] += std::pow((current_result[observation_index][snapshot_index] - local_meanvalue[observation_index]), 2);</div><div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;            variance_new[observation_index] = SMAX((variance_new[observation_index] / (this-&gt;snapshot_ - snapshot_for_converged_)), variance[observation_index], std::pow(local_meanvalue[observation_index] * 1.0e-2, 2));</div><div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;        }</div><div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;    };</div><div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;    <span class="comment">//=================================================================================================//</span></div><div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;    <span class="keyword">template</span>&lt;<span class="keyword">class</span> ObserveMethodType&gt;</div><div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;    <span class="keywordtype">void</span> <a class="code" href="class_s_p_h_1_1_regression_test_time_averaged.html#a9e897ed946fa84afe6c2624a5b46f80b">RegressionTestTimeAveraged&lt;ObserveMethodType&gt;::calculateNewVariance</a>(DoubleVec&lt;Vecd&gt; &amp;current_result, </div><div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;        StdVec&lt;Vecd&gt; &amp;local_meanvalue, StdVec&lt;Vecd&gt; &amp;variance, StdVec&lt;Vecd&gt; &amp;variance_new)</div><div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;    {</div><div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> observation_index = 0; observation_index != this-&gt;observation_; ++observation_index)</div><div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;            <span class="keywordflow">for</span> (<span class="keywordtype">int</span> dimension_index = 0; dimension_index != current_result[0][0].size(); ++dimension_index)</div><div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;            {</div><div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;                <span class="keywordflow">for</span> (<span class="keywordtype">int</span> snapshot_index = snapshot_for_converged_; snapshot_index != this-&gt;snapshot_; ++snapshot_index)</div><div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;                    variance_new[observation_index][dimension_index] += std::pow((current_result[observation_index][snapshot_index][dimension_index] - local_meanvalue[observation_index][dimension_index]), 2);</div><div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;                variance_new[observation_index][dimension_index] = SMAX((variance_new[observation_index][dimension_index] / (this-&gt;snapshot_ - snapshot_for_converged_)), variance[observation_index][dimension_index], std::pow(local_meanvalue[observation_index][dimension_index] * 1.0e-2, 2));</div><div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;            }</div><div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;    };</div><div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;    <span class="comment">//=================================================================================================//</span></div><div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;    <span class="keyword">template</span>&lt;<span class="keyword">class</span> ObserveMethodType&gt;</div><div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;    <span class="keywordtype">void</span> <a class="code" href="class_s_p_h_1_1_regression_test_time_averaged.html#a9e897ed946fa84afe6c2624a5b46f80b">RegressionTestTimeAveraged&lt;ObserveMethodType&gt;::calculateNewVariance</a>(DoubleVec&lt;Matd&gt; &amp;current_result, </div><div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;        StdVec&lt;Matd&gt; &amp;local_meanvalue, StdVec&lt;Matd&gt; &amp;variance, StdVec&lt;Matd&gt; &amp;variance_new)</div><div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;    {</div><div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> observation_index = 0; observation_index != this-&gt;observation_; ++observation_index)</div><div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;            <span class="keywordflow">for</span> (<span class="keywordtype">int</span> dimension_index_i = 0; dimension_index_i != current_result[0][0].size(); ++dimension_index_i)</div><div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;                <span class="keywordflow">for</span> (<span class="keywordtype">int</span> dimension_index_j = 0; dimension_index_j != current_result[0][0].size(); ++dimension_index_j)</div><div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;                {</div><div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;                    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> snapshot_index = snapshot_for_converged_; snapshot_index != this-&gt;snapshot_; ++snapshot_index)</div><div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;                        variance_new[observation_index][dimension_index_i][dimension_index_j] += std::pow((current_result[observation_index][snapshot_index][dimension_index_i][dimension_index_j] - local_meanvalue[observation_index][dimension_index_i][dimension_index_j]), 2);</div><div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;                    variance_new[observation_index][dimension_index_i][dimension_index_j] = SMAX((variance_new[observation_index][dimension_index_i][dimension_index_j] / (this-&gt;snapshot_ - snapshot_for_converged_)), variance[observation_index][dimension_index_i][dimension_index_j], std::pow(local_meanvalue[observation_index][dimension_index_i][dimension_index_j] * 1.0e-2, 2));</div><div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;                }</div><div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;    };</div><div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;    <span class="comment">//=================================================================================================//</span></div><div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;    <span class="keyword">template</span>&lt;<span class="keyword">class</span> ObserveMethodType&gt;</div><div class="line"><a name="l00230"></a><span class="lineno"><a class="line" href="class_s_p_h_1_1_regression_test_time_averaged.html#a03c15934c73e44ef45c11475c3245d71">  230</a></span>&#160;    <span class="keywordtype">int</span> <a class="code" href="class_s_p_h_1_1_regression_test_time_averaged.html#a03c15934c73e44ef45c11475c3245d71">RegressionTestTimeAveraged&lt;ObserveMethodType&gt;::compareParameter</a>(<span class="keywordtype">string</span> par_name,</div><div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;        <a class="code" href="class_std_vec_3_01_real_01_4.html">StdVec&lt;Real&gt;</a> &amp;parameter, <a class="code" href="class_std_vec_3_01_real_01_4.html">StdVec&lt;Real&gt;</a> &amp;parameter_new, Real &amp;threshold)</div><div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;    {</div><div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;        <span class="keywordtype">int</span> count = 0;</div><div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> observation_index = 0; observation_index != this-&gt;observation_; ++observation_index)</div><div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;        {</div><div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;            <span class="keywordflow">if</span> ((par_name == <span class="stringliteral">&quot;meanvalue&quot;</span>) &amp;&amp; (ABS(parameter[observation_index]) &lt; 0.005) &amp;&amp; (ABS(parameter_new[observation_index]) &lt; 0.005))</div><div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;            {</div><div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;                std::cout &lt;&lt; <span class="stringliteral">&quot;The old meanvalue is &quot;</span> &lt;&lt; parameter[observation_index] &lt;&lt; <span class="stringliteral">&quot;, and the new meanvalue is &quot;</span> &lt;&lt; parameter_new[observation_index]</div><div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;                    &lt;&lt; <span class="stringliteral">&quot;. So this variable will be ignored due to its tiny effect.&quot;</span> &lt;&lt; endl;</div><div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;                <span class="keywordflow">continue</span>;</div><div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;            }</div><div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;            Real relative_value_ = ABS((parameter[observation_index] - parameter_new[observation_index]) / (parameter_new[observation_index] + TinyReal));</div><div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;            <span class="keywordflow">if</span> (relative_value_ &gt; threshold)</div><div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;            {</div><div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;                std::cout &lt;&lt; par_name &lt;&lt; <span class="stringliteral">&quot;: &quot;</span> &lt;&lt; this-&gt;quantity_name_ &lt;&lt; <span class="stringliteral">&quot;[&quot;</span> &lt;&lt; observation_index &lt;&lt; <span class="stringliteral">&quot;]&quot;</span></div><div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;                    &lt;&lt; <span class="stringliteral">&quot; is not converged, and difference is &quot;</span> &lt;&lt; relative_value_ &lt;&lt; endl;</div><div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;                count++;</div><div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;            }</div><div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;        }</div><div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;        <span class="keywordflow">return</span> count;</div><div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;    };</div><div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;    <span class="comment">//=================================================================================================//</span></div><div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;    <span class="keyword">template</span>&lt;<span class="keyword">class</span> ObserveMethodType&gt;</div><div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;    <span class="keywordtype">int</span> <a class="code" href="class_s_p_h_1_1_regression_test_time_averaged.html#a03c15934c73e44ef45c11475c3245d71">RegressionTestTimeAveraged&lt;ObserveMethodType&gt;::compareParameter</a>(<span class="keywordtype">string</span> par_name,</div><div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;        StdVec&lt;Vecd&gt; &amp;parameter, StdVec&lt;Vecd&gt; &amp;parameter_new, Vecd &amp;threshold)</div><div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;    {</div><div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;        <span class="keywordtype">int</span> count = 0;</div><div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> observation_index = 0; observation_index != this-&gt;observation_; ++observation_index)</div><div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;            <span class="keywordflow">for</span> (<span class="keywordtype">int</span> dimension_index = 0; dimension_index != parameter[0].size(); ++dimension_index)</div><div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;            {</div><div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;                <span class="keywordflow">if</span> ((par_name == <span class="stringliteral">&quot;meanvalue&quot;</span>) &amp;&amp; (ABS(parameter[observation_index][dimension_index]) &lt; 0.001) &amp;&amp; (ABS(parameter_new[observation_index][dimension_index]) &lt; 0.001))</div><div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;                {</div><div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;                    std::cout &lt;&lt; <span class="stringliteral">&quot;The old meanvalue is &quot;</span> &lt;&lt; parameter[observation_index][dimension_index] &lt;&lt; <span class="stringliteral">&quot;, and the new meanvalue is &quot;</span> &lt;&lt; parameter_new[observation_index][dimension_index]</div><div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;                        &lt;&lt; <span class="stringliteral">&quot;. So this variable will be ignored due to its tiny effect.&quot;</span> &lt;&lt; endl;</div><div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;                    <span class="keywordflow">continue</span>;</div><div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;                }</div><div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;                Real relative_value_ = ABS((parameter[observation_index][dimension_index] - parameter_new[observation_index][dimension_index]) / (parameter_new[observation_index][dimension_index] + TinyReal));</div><div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;                <span class="keywordflow">if</span> (relative_value_ &gt; threshold[dimension_index])</div><div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;                {</div><div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;                    std::cout &lt;&lt; par_name &lt;&lt; <span class="stringliteral">&quot;: &quot;</span> &lt;&lt; this-&gt;quantity_name_ &lt;&lt; <span class="stringliteral">&quot;[&quot;</span> &lt;&lt; observation_index &lt;&lt; <span class="stringliteral">&quot;][&quot;</span> &lt;&lt; dimension_index &lt;&lt; <span class="stringliteral">&quot;]&quot;</span></div><div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;                        &lt;&lt; <span class="stringliteral">&quot; is not converged, and difference is &quot;</span> &lt;&lt; relative_value_ &lt;&lt; endl;</div><div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;                    count++;</div><div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;                }</div><div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;            }</div><div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;        <span class="keywordflow">return</span> count;</div><div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;    };</div><div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;    <span class="comment">//=================================================================================================//</span></div><div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;    <span class="keyword">template</span>&lt;<span class="keyword">class</span> ObserveMethodType&gt;</div><div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;    <span class="keywordtype">int</span> <a class="code" href="class_s_p_h_1_1_regression_test_time_averaged.html#a03c15934c73e44ef45c11475c3245d71">RegressionTestTimeAveraged&lt;ObserveMethodType&gt;::compareParameter</a>(<span class="keywordtype">string</span> par_name,</div><div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;        StdVec&lt;Matd&gt; &amp;parameter, StdVec&lt;Matd&gt; &amp;parameter_new, Matd &amp;threshold)</div><div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;    {</div><div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;        <span class="keywordtype">int</span> count = 0;</div><div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> observation_index = 0; observation_index != this-&gt;observation_; ++observation_index)</div><div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;            <span class="keywordflow">for</span> (<span class="keywordtype">int</span> dimension_index_i = 0; dimension_index_i != parameter[0].size(); ++dimension_index_i)</div><div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;                <span class="keywordflow">for</span> (<span class="keywordtype">int</span> dimension_index_j = 0; dimension_index_j != parameter[0].size(); ++dimension_index_i)</div><div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;                {</div><div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;                    <span class="keywordflow">if</span> ((par_name == <span class="stringliteral">&quot;meanvalue&quot;</span>) &amp;&amp; (ABS(parameter[observation_index][dimension_index_i][dimension_index_j]) &lt; 0.001) &amp;&amp; (ABS(parameter_new[observation_index][dimension_index_i][dimension_index_j]) &lt; 0.001))</div><div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;                    {</div><div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;                        std::cout &lt;&lt; <span class="stringliteral">&quot;The old meanvalue is &quot;</span> &lt;&lt; parameter[observation_index][dimension_index_i][dimension_index_j] &lt;&lt; <span class="stringliteral">&quot;, and the new meanvalue is &quot;</span> &lt;&lt; parameter_new[observation_index][dimension_index_i][dimension_index_j] </div><div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;                            &lt;&lt; <span class="stringliteral">&quot;. So this variable will be ignored due to its tiny effect.&quot;</span> &lt;&lt; endl;</div><div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;                        <span class="keywordflow">continue</span>;</div><div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;                    }</div><div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;                    Real relative_value_ = ABS((parameter[observation_index][dimension_index_i][dimension_index_j] - parameter_new[observation_index][dimension_index_i][dimension_index_j]) / (parameter_new[observation_index][dimension_index_i][dimension_index_j] + TinyReal));</div><div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;                    <span class="keywordflow">if</span> (relative_value_ &gt; threshold[dimension_index_i][dimension_index_j])</div><div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;                    {</div><div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;                        std::cout &lt;&lt; par_name &lt;&lt; <span class="stringliteral">&quot;: &quot;</span> &lt;&lt; this-&gt;quantity_name_ &lt;&lt; <span class="stringliteral">&quot;[&quot;</span> &lt;&lt; observation_index &lt;&lt; <span class="stringliteral">&quot;][&quot;</span> &lt;&lt; dimension_index_i &lt;&lt; <span class="stringliteral">&quot;][&quot;</span> &lt;&lt; dimension_index_j &lt;&lt; <span class="stringliteral">&quot;]&quot;</span></div><div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;                            &lt;&lt; <span class="stringliteral">&quot; is not converged, and difference is &quot;</span> &lt;&lt; relative_value_ &lt;&lt; endl;</div><div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;                        count++;</div><div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;                    }</div><div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;                }</div><div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;        <span class="keywordflow">return</span> count;</div><div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;    };</div><div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;    <span class="comment">//=================================================================================================//</span></div><div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;    <span class="keyword">template</span>&lt;<span class="keyword">class</span> ObserveMethodType&gt;</div><div class="line"><a name="l00305"></a><span class="lineno"><a class="line" href="class_s_p_h_1_1_regression_test_time_averaged.html#aa72b7029d93a690ff6699be64c76d3b9">  305</a></span>&#160;    <span class="keywordtype">int</span> <a class="code" href="class_s_p_h_1_1_regression_test_time_averaged.html#aa72b7029d93a690ff6699be64c76d3b9">RegressionTestTimeAveraged&lt;ObserveMethodType&gt;::testNewResult</a>(DoubleVec&lt;Real&gt; &amp;current_result, </div><div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;        <a class="code" href="class_std_vec_3_01_real_01_4.html">StdVec&lt;Real&gt;</a> &amp;meanvalue, <a class="code" href="class_std_vec_3_01_real_01_4.html">StdVec&lt;Real&gt;</a> &amp;local_meanvalue, <a class="code" href="class_std_vec_3_01_real_01_4.html">StdVec&lt;Real&gt;</a> &amp;variance)</div><div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;    {</div><div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;        <span class="keywordtype">int</span> count = 0;</div><div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> observation_index = 0; observation_index != this-&gt;observation_; ++observation_index)</div><div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;        {</div><div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;            <span class="keywordflow">for</span> (<span class="keywordtype">int</span> snapshot_index = snapshot_for_converged_; snapshot_index != this-&gt;snapshot_; ++snapshot_index)</div><div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;            {</div><div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;                variance_new_[observation_index] += std::pow((current_result[snapshot_index][observation_index] - local_meanvalue[observation_index]), 2);</div><div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;            }</div><div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;            variance_new_[observation_index] = variance_new_[observation_index] / (this-&gt;snapshot_ - snapshot_for_converged_);</div><div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;            <span class="keywordflow">if</span> ((ABS(meanvalue[observation_index]) &lt; 0.005) &amp;&amp; (ABS(local_meanvalue[observation_index]) &lt; 0.005))</div><div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;            {</div><div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;                std::cout &lt;&lt; <span class="stringliteral">&quot;The old meanvalue is &quot;</span> &lt;&lt; meanvalue[observation_index] &lt;&lt; <span class="stringliteral">&quot;, and the current meanvalue is &quot;</span> &lt;&lt; local_meanvalue[observation_index]</div><div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;                    &lt;&lt; <span class="stringliteral">&quot;. So this variable will not be tested due to its tiny effect.&quot;</span> &lt;&lt; endl;</div><div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;                <span class="keywordflow">continue</span>;</div><div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;            }</div><div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;            Real relative_value_ = ABS((meanvalue[observation_index] - local_meanvalue[observation_index]) / meanvalue[observation_index]);</div><div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;            <span class="keywordflow">if</span> (relative_value_ &gt; 0.1 || variance_new_[observation_index] &gt; (1.01 * variance[observation_index]))</div><div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;            {</div><div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;                std::cout &lt;&lt; this-&gt;quantity_name_ &lt;&lt; <span class="stringliteral">&quot;[&quot;</span> &lt;&lt; observation_index &lt;&lt; <span class="stringliteral">&quot;] is beyond the exception !&quot;</span> &lt;&lt; endl;</div><div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;                std::cout &lt;&lt; <span class="stringliteral">&quot;The meanvalue is &quot;</span> &lt;&lt; meanvalue[observation_index] &lt;&lt; <span class="stringliteral">&quot;, and the current meanvalue is &quot;</span> &lt;&lt; local_meanvalue[observation_index] &lt;&lt; endl;</div><div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;                std::cout &lt;&lt; <span class="stringliteral">&quot;The variance is &quot;</span> &lt;&lt; variance[observation_index] &lt;&lt; <span class="stringliteral">&quot;, and the current variance is &quot;</span> &lt;&lt; variance_new_[observation_index] &lt;&lt; endl;</div><div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;                count++;</div><div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;            }</div><div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;        }</div><div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;        <span class="keywordflow">return</span> count;</div><div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;    };</div><div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;    <span class="comment">//=================================================================================================//</span></div><div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;    <span class="keyword">template</span>&lt;<span class="keyword">class</span> ObserveMethodType&gt;</div><div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;    <span class="keywordtype">int</span> <a class="code" href="class_s_p_h_1_1_regression_test_time_averaged.html#aa72b7029d93a690ff6699be64c76d3b9">RegressionTestTimeAveraged&lt;ObserveMethodType&gt;::testNewResult</a>(DoubleVec&lt;Vecd&gt; &amp;current_result, </div><div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;        StdVec&lt;Vecd&gt; &amp;meanvalue, StdVec&lt;Vecd&gt; &amp;local_meanvalue, StdVec&lt;Vecd&gt; &amp;variance)</div><div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;    {</div><div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;        <span class="keywordtype">int</span> count = 0;</div><div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> observation_index = 0; observation_index != this-&gt;observation_; ++observation_index)</div><div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;        {</div><div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;            <span class="keywordflow">for</span> (<span class="keywordtype">int</span> dimension_index_i = 0; dimension_index_i != meanvalue_[0].size(); ++dimension_index_i)</div><div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;            {</div><div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;                <span class="keywordflow">for</span> (<span class="keywordtype">int</span> snapshot_index = snapshot_for_converged_; snapshot_index != this-&gt;snapshot_; ++snapshot_index)</div><div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;                {</div><div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160;                    variance_new_[observation_index][dimension_index_i] += std::pow((current_result[snapshot_index][observation_index][dimension_index_i] - local_meanvalue[observation_index][dimension_index_i]), 2);</div><div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;                }</div><div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160;                variance_new_[observation_index][dimension_index_i] = variance_new_[observation_index][dimension_index_i] / (this-&gt;snapshot_ - snapshot_for_converged_);</div><div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160;                <span class="keywordflow">if</span> ((ABS(meanvalue[observation_index][dimension_index_i]) &lt; 0.005) &amp;&amp; (ABS(local_meanvalue[observation_index][dimension_index_i]) &lt; 0.005))</div><div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;                {</div><div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;                    std::cout &lt;&lt; <span class="stringliteral">&quot;The old meanvalue is &quot;</span> &lt;&lt; meanvalue[observation_index][dimension_index_i] &lt;&lt; <span class="stringliteral">&quot;, and the current meanvalue is &quot;</span> &lt;&lt; local_meanvalue[observation_index][dimension_index_i]</div><div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160;                        &lt;&lt; <span class="stringliteral">&quot;. So this variable will not be tested due to its tiny effect.&quot;</span> &lt;&lt; endl;</div><div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160;                    <span class="keywordflow">continue</span>;</div><div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;                }</div><div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;                Real relative_value_ = ABS((meanvalue[observation_index][dimension_index_i] - local_meanvalue[observation_index][dimension_index_i]) / meanvalue[observation_index][dimension_index_i]);</div><div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;                <span class="keywordflow">if</span> (relative_value_ &gt; 0.1 || (variance_new_[observation_index][dimension_index_i] &gt; 1.01 * variance[observation_index][dimension_index_i]))</div><div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;                {</div><div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;                    std::cout &lt;&lt; this-&gt;quantity_name_ &lt;&lt; <span class="stringliteral">&quot;[&quot;</span> &lt;&lt; observation_index &lt;&lt; <span class="stringliteral">&quot;][&quot;</span> &lt;&lt; dimension_index_i &lt;&lt; <span class="stringliteral">&quot;] is beyond the exception !&quot;</span> &lt;&lt; endl;</div><div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;                    std::cout &lt;&lt; <span class="stringliteral">&quot;The meanvalue is &quot;</span> &lt;&lt; meanvalue[observation_index][dimension_index_i] &lt;&lt; <span class="stringliteral">&quot;, and the current meanvalue is &quot;</span> &lt;&lt; local_meanvalue[observation_index][dimension_index_i] &lt;&lt; endl;</div><div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;                    std::cout &lt;&lt; <span class="stringliteral">&quot;The variance is &quot;</span> &lt;&lt; variance[observation_index][dimension_index_i] &lt;&lt; <span class="stringliteral">&quot;, and the new variance is &quot;</span> &lt;&lt; variance_new_[observation_index][dimension_index_i] &lt;&lt; endl;</div><div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160;                    count++;</div><div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160;                }</div><div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;            }</div><div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160;        }</div><div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;        <span class="keywordflow">return</span> count;</div><div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;    };</div><div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;    <span class="comment">//=================================================================================================// </span></div><div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160;    <span class="keyword">template</span>&lt;<span class="keyword">class</span> ObserveMethodType&gt;</div><div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;    <span class="keywordtype">int</span> <a class="code" href="class_s_p_h_1_1_regression_test_time_averaged.html#aa72b7029d93a690ff6699be64c76d3b9">RegressionTestTimeAveraged&lt;ObserveMethodType&gt;::testNewResult</a>(DoubleVec&lt;Matd&gt; &amp;current_result, </div><div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160;        StdVec&lt;Matd&gt; &amp;meanvalue, StdVec&lt;Matd&gt; &amp;local_meanvalue, StdVec&lt;Matd&gt; &amp;variance)</div><div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160;    {</div><div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160;        <span class="keywordtype">int</span> count = 0;</div><div class="line"><a name="l00372"></a><span class="lineno">  372</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> observation_index = 0; observation_index != this-&gt;observation_; ++observation_index)</div><div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160;        {</div><div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;            <span class="keywordflow">for</span> (<span class="keywordtype">int</span> dimension_index_i = 0; dimension_index_i != meanvalue[0].size(); ++dimension_index_i)</div><div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160;            {</div><div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160;                <span class="keywordflow">for</span> (<span class="keywordtype">int</span> dimension_index_j = 0; dimension_index_j != meanvalue[0].size(); ++dimension_index_j)</div><div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;                {</div><div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;                    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> snapshot_index = snapshot_for_converged_; snapshot_index != this-&gt;snapshot_; ++snapshot_index)</div><div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160;                    {</div><div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;                        variance_new_[observation_index][dimension_index_i][dimension_index_j] += std::pow((current_result[snapshot_index][observation_index][dimension_index_i][dimension_index_j] - local_meanvalue[observation_index][dimension_index_i][dimension_index_j]), 2);</div><div class="line"><a name="l00381"></a><span class="lineno">  381</span>&#160;                    }</div><div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160;                    variance_new_[observation_index][dimension_index_i][dimension_index_j] = variance_new_[observation_index][dimension_index_i][dimension_index_j] / (this-&gt;snapshot_ - snapshot_for_converged_);</div><div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160;                    <span class="keywordflow">if</span> ((ABS(meanvalue[observation_index][dimension_index_i][dimension_index_j]) &lt; 0.005) &amp;&amp; (ABS(local_meanvalue[observation_index][dimension_index_i][dimension_index_j]) &lt; 0.005))</div><div class="line"><a name="l00384"></a><span class="lineno">  384</span>&#160;                    {</div><div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160;                        std::cout &lt;&lt; <span class="stringliteral">&quot;The old meanvalue is &quot;</span> &lt;&lt; meanvalue[observation_index][dimension_index_i][dimension_index_j] &lt;&lt; <span class="stringliteral">&quot;, and the new meanvalue is &quot;</span> &lt;&lt; local_meanvalue[observation_index][dimension_index_i][dimension_index_j]</div><div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160;                            &lt;&lt; <span class="stringliteral">&quot;. So this variable will not be tested due to its tiny effect. &quot;</span> &lt;&lt; endl;</div><div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;                        <span class="keywordflow">continue</span>;</div><div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160;                    }</div><div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160;                    Real relative_value_ = ABS((meanvalue_[observation_index][dimension_index_i][dimension_index_j] - local_meanvalue[observation_index][dimension_index_i][dimension_index_j]) / meanvalue[observation_index][dimension_index_i][dimension_index_j]);</div><div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160;                    <span class="keywordflow">if</span> (relative_value_ &gt; 0.1 || variance_new_[observation_index][dimension_index_i][dimension_index_j] &gt; 1.01 * variance[observation_index][dimension_index_i][dimension_index_j])</div><div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160;                    {</div><div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160;                        std::cout &lt;&lt; this-&gt;quantity_name_ &lt;&lt; <span class="stringliteral">&quot;[&quot;</span> &lt;&lt; observation_index &lt;&lt; <span class="stringliteral">&quot;][&quot;</span> &lt;&lt; dimension_index_i &lt;&lt; <span class="stringliteral">&quot;][&quot;</span> &lt;&lt; dimension_index_j &lt;&lt; <span class="stringliteral">&quot;] is beyond the exception !&quot;</span> &lt;&lt; endl;</div><div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;                        std::cout &lt;&lt; <span class="stringliteral">&quot;The meanvalue is &quot;</span> &lt;&lt; meanvalue[observation_index][dimension_index_i][dimension_index_j] &lt;&lt; <span class="stringliteral">&quot;, and the new meanvalue is &quot;</span> &lt;&lt; local_meanvalue[observation_index][dimension_index_i][dimension_index_j] &lt;&lt; endl;</div><div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160;                        std::cout &lt;&lt; <span class="stringliteral">&quot;The variance is &quot;</span> &lt;&lt; variance[observation_index][dimension_index_i][dimension_index_j] &lt;&lt; <span class="stringliteral">&quot;, and the new variance is &quot;</span> &lt;&lt; variance_new_[observation_index][dimension_index_i][dimension_index_j] &lt;&lt; endl;</div><div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160;                        count++;</div><div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160;                    }</div><div class="line"><a name="l00397"></a><span class="lineno">  397</span>&#160;                }</div><div class="line"><a name="l00398"></a><span class="lineno">  398</span>&#160;            }</div><div class="line"><a name="l00399"></a><span class="lineno">  399</span>&#160;        }</div><div class="line"><a name="l00400"></a><span class="lineno">  400</span>&#160;        <span class="keywordflow">return</span> count;</div><div class="line"><a name="l00401"></a><span class="lineno">  401</span>&#160;    };</div><div class="line"><a name="l00402"></a><span class="lineno">  402</span>&#160;    <span class="comment">//=================================================================================================//   </span></div><div class="line"><a name="l00403"></a><span class="lineno">  403</span>&#160;    <span class="keyword">template</span>&lt;<span class="keyword">class</span> ObserveMethodType&gt;</div><div class="line"><a name="l00404"></a><span class="lineno"><a class="line" href="class_s_p_h_1_1_regression_test_time_averaged.html#a622f8618bab75f36e56a713bf4e89615">  404</a></span>&#160;    <span class="keywordtype">void</span> <a class="code" href="class_s_p_h_1_1_regression_test_time_averaged.html#a622f8618bab75f36e56a713bf4e89615">RegressionTestTimeAveraged&lt;ObserveMethodType&gt;::initializeThreshold</a>(VariableType &amp;threshold_mean, VariableType &amp;threshold_variance)</div><div class="line"><a name="l00405"></a><span class="lineno">  405</span>&#160;    {</div><div class="line"><a name="l00406"></a><span class="lineno">  406</span>&#160;        threshold_mean_ = threshold_mean;</div><div class="line"><a name="l00407"></a><span class="lineno">  407</span>&#160;        threshold_variance_ = threshold_variance;</div><div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160;    };</div><div class="line"><a name="l00409"></a><span class="lineno">  409</span>&#160;    <span class="comment">//=================================================================================================//</span></div><div class="line"><a name="l00410"></a><span class="lineno">  410</span>&#160;    <span class="keyword">template</span>&lt;<span class="keyword">class</span> ObserveMethodType&gt;</div><div class="line"><a name="l00411"></a><span class="lineno">  411</span>&#160;    <span class="keywordtype">void</span> <a class="code" href="class_s_p_h_1_1_regression_test_time_averaged.html">RegressionTestTimeAveraged&lt;ObserveMethodType&gt;::settingupTheTest</a>()</div><div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160;    {</div><div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160;        this-&gt;snapshot_ = this-&gt;current_result_.size();</div><div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160;        this-&gt;observation_ = this-&gt;current_result_[0].size();</div><div class="line"><a name="l00415"></a><span class="lineno">  415</span>&#160;        StdVec&lt;VariableType&gt; temp(this-&gt;observation_);</div><div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160;        meanvalue_ = temp;</div><div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160;        variance_ = temp;</div><div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160;        local_meanvalue_ = temp;</div><div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;        meanvalue_new_ = meanvalue_;</div><div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160;        variance_new_ = variance_;</div><div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160;</div><div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160;        <span class="keywordflow">if</span> ((this-&gt;number_of_run_ &gt; 1) &amp;&amp; (!fs::exists(mean_variance_filefullpath_)))</div><div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160;        {</div><div class="line"><a name="l00424"></a><span class="lineno">  424</span>&#160;            std::cout &lt;&lt; <span class="stringliteral">&quot;\n Error: the input file:&quot;</span> &lt;&lt; mean_variance_filefullpath_ &lt;&lt; <span class="stringliteral">&quot; is not exists&quot;</span> &lt;&lt; std::endl;</div><div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;            std::cout &lt;&lt; __FILE__ &lt;&lt; <span class="charliteral">&#39;:&#39;</span> &lt;&lt; __LINE__ &lt;&lt; std::endl;</div><div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160;            exit(1);</div><div class="line"><a name="l00427"></a><span class="lineno">  427</span>&#160;        }</div><div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160;    };</div><div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;    <span class="comment">//=================================================================================================//</span></div><div class="line"><a name="l00430"></a><span class="lineno">  430</span>&#160;    <span class="keyword">template</span>&lt;<span class="keyword">class</span> ObserveMethodType&gt;</div><div class="line"><a name="l00431"></a><span class="lineno"><a class="line" href="class_s_p_h_1_1_regression_test_time_averaged.html#a6a84508bedab4d15b399451f2a37462b">  431</a></span>&#160;    <span class="keywordtype">void</span> <a class="code" href="class_s_p_h_1_1_regression_test_time_averaged.html#a6a84508bedab4d15b399451f2a37462b">RegressionTestTimeAveraged&lt;ObserveMethodType&gt;::readMeanVarianceFromXml</a>()</div><div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160;    {</div><div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160;        <span class="keywordflow">if</span> (this-&gt;number_of_run_ &gt; 1)</div><div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160;        {</div><div class="line"><a name="l00435"></a><span class="lineno">  435</span>&#160;            mean_variance_xml_engine_in_.loadXmlFile(mean_variance_filefullpath_);</div><div class="line"><a name="l00436"></a><span class="lineno">  436</span>&#160;            SimTK::Xml::Element meanvalue_element_ = mean_variance_xml_engine_in_.getChildElement(<span class="stringliteral">&quot;MeanValue_Element&quot;</span>);</div><div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160;            SimTK::Xml::element_iterator ele_ite_mean_ = meanvalue_element_.element_begin();</div><div class="line"><a name="l00438"></a><span class="lineno">  438</span>&#160;            <span class="keywordflow">for</span> (; ele_ite_mean_ != meanvalue_element_.element_end(); ++ele_ite_mean_)</div><div class="line"><a name="l00439"></a><span class="lineno">  439</span>&#160;                <span class="keywordflow">for</span> (<span class="keywordtype">int</span> observation_index = 0; observation_index != this-&gt;observation_; ++observation_index)</div><div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160;                {</div><div class="line"><a name="l00441"></a><span class="lineno">  441</span>&#160;                    std::string attribute_name_ = this-&gt;quantity_name_ + <span class="stringliteral">&quot;_&quot;</span> + std::to_string(observation_index);</div><div class="line"><a name="l00442"></a><span class="lineno">  442</span>&#160;                    mean_variance_xml_engine_in_.getRequiredAttributeValue(ele_ite_mean_, attribute_name_, meanvalue_[observation_index]);</div><div class="line"><a name="l00443"></a><span class="lineno">  443</span>&#160;                }</div><div class="line"><a name="l00444"></a><span class="lineno">  444</span>&#160;</div><div class="line"><a name="l00445"></a><span class="lineno">  445</span>&#160;            SimTK::Xml::Element variance_element_ = mean_variance_xml_engine_in_.getChildElement(<span class="stringliteral">&quot;Variance_Element&quot;</span>);</div><div class="line"><a name="l00446"></a><span class="lineno">  446</span>&#160;            SimTK::Xml::element_iterator ele_ite_variance_ = variance_element_.element_begin();</div><div class="line"><a name="l00447"></a><span class="lineno">  447</span>&#160;            <span class="keywordflow">for</span> (; ele_ite_variance_ != variance_element_.element_end(); ++ele_ite_variance_)</div><div class="line"><a name="l00448"></a><span class="lineno">  448</span>&#160;                <span class="keywordflow">for</span> (<span class="keywordtype">int</span> observation_index = 0; observation_index != this-&gt;observation_; ++observation_index)</div><div class="line"><a name="l00449"></a><span class="lineno">  449</span>&#160;                {</div><div class="line"><a name="l00450"></a><span class="lineno">  450</span>&#160;                    std::string attribute_name_ = this-&gt;quantity_name_ + <span class="stringliteral">&quot;_&quot;</span> + std::to_string(observation_index);</div><div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160;                    mean_variance_xml_engine_in_.getRequiredAttributeValue(ele_ite_variance_, attribute_name_, variance_[observation_index]);</div><div class="line"><a name="l00452"></a><span class="lineno">  452</span>&#160;                }</div><div class="line"><a name="l00453"></a><span class="lineno">  453</span>&#160;        }</div><div class="line"><a name="l00454"></a><span class="lineno">  454</span>&#160;    };</div><div class="line"><a name="l00455"></a><span class="lineno">  455</span>&#160;    <span class="comment">//=================================================================================================//</span></div><div class="line"><a name="l00456"></a><span class="lineno">  456</span>&#160;    <span class="keyword">template</span>&lt;<span class="keyword">class</span> ObserveMethodType&gt;</div><div class="line"><a name="l00457"></a><span class="lineno"><a class="line" href="class_s_p_h_1_1_regression_test_time_averaged.html#aea0ca51a8c49bd413314ff5fbfff5904">  457</a></span>&#160;    <span class="keywordtype">void</span> <a class="code" href="class_s_p_h_1_1_regression_test_time_averaged.html#aea0ca51a8c49bd413314ff5fbfff5904">RegressionTestTimeAveraged&lt;ObserveMethodType&gt;::searchForStartPoint</a>()</div><div class="line"><a name="l00458"></a><span class="lineno">  458</span>&#160;    {</div><div class="line"><a name="l00459"></a><span class="lineno">  459</span>&#160;        snapshot_for_converged_ = 0;</div><div class="line"><a name="l00460"></a><span class="lineno">  460</span>&#160;        searchSteadyStart(this-&gt;current_result_);</div><div class="line"><a name="l00461"></a><span class="lineno">  461</span>&#160;        std::cout &lt;&lt; <span class="stringliteral">&quot;The snapshot for converged is &quot;</span> &lt;&lt; snapshot_for_converged_ &lt;&lt; endl;</div><div class="line"><a name="l00462"></a><span class="lineno">  462</span>&#160;    };</div><div class="line"><a name="l00463"></a><span class="lineno">  463</span>&#160;    <span class="comment">//=================================================================================================//</span></div><div class="line"><a name="l00464"></a><span class="lineno">  464</span>&#160;    <span class="keyword">template</span>&lt;<span class="keyword">class</span> ObserveMethodType&gt;</div><div class="line"><a name="l00465"></a><span class="lineno"><a class="line" href="class_s_p_h_1_1_regression_test_time_averaged.html#a1b4e1fb0a6f9454834003c3f246a2fac">  465</a></span>&#160;    <span class="keywordtype">void</span> <a class="code" href="class_s_p_h_1_1_regression_test_time_averaged.html#a1b4e1fb0a6f9454834003c3f246a2fac">RegressionTestTimeAveraged&lt;ObserveMethodType&gt;::filterExtremeValues</a>()</div><div class="line"><a name="l00466"></a><span class="lineno">  466</span>&#160;    {</div><div class="line"><a name="l00467"></a><span class="lineno">  467</span>&#160;        filterLocalResult(this-&gt;current_result_);</div><div class="line"><a name="l00468"></a><span class="lineno">  468</span>&#160;        filefullpath_filter_output_ = this-&gt;input_folder_path_ + <span class="stringliteral">&quot;/&quot;</span> + this-&gt;body_name_</div><div class="line"><a name="l00469"></a><span class="lineno">  469</span>&#160;            + <span class="stringliteral">&quot;_&quot;</span> + this-&gt;quantity_name_ + <span class="stringliteral">&quot;.dat&quot;</span>;</div><div class="line"><a name="l00470"></a><span class="lineno">  470</span>&#160;        std::ofstream out_file(filefullpath_filter_output_.c_str(), std::ios::app);</div><div class="line"><a name="l00471"></a><span class="lineno">  471</span>&#160;        out_file &lt;&lt; <span class="stringliteral">&quot;run_time&quot;</span> &lt;&lt; <span class="stringliteral">&quot;   &quot;</span>;</div><div class="line"><a name="l00472"></a><span class="lineno">  472</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> observation_index = 0;  observation_index != this-&gt;observation_; ++observation_index)</div><div class="line"><a name="l00473"></a><span class="lineno">  473</span>&#160;        {</div><div class="line"><a name="l00474"></a><span class="lineno">  474</span>&#160;            std::string quantity_name_i = this-&gt;quantity_name_ + <span class="stringliteral">&quot;[&quot;</span> + std::to_string(observation_index) + <span class="stringliteral">&quot;]&quot;</span>;</div><div class="line"><a name="l00475"></a><span class="lineno">  475</span>&#160;            this-&gt;plt_engine_.writeAQuantityHeader(out_file, this-&gt;current_result_[0][0], quantity_name_i);</div><div class="line"><a name="l00476"></a><span class="lineno">  476</span>&#160;        }</div><div class="line"><a name="l00477"></a><span class="lineno">  477</span>&#160;        out_file &lt;&lt; <span class="stringliteral">&quot;\n&quot;</span>;</div><div class="line"><a name="l00478"></a><span class="lineno">  478</span>&#160;        out_file.close();</div><div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160;</div><div class="line"><a name="l00480"></a><span class="lineno">  480</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> snapshot_index = 0; snapshot_index != this-&gt;snapshot_; ++snapshot_index)</div><div class="line"><a name="l00481"></a><span class="lineno">  481</span>&#160;        {</div><div class="line"><a name="l00482"></a><span class="lineno">  482</span>&#160;            std::ofstream out_file(filefullpath_filter_output_.c_str(), std::ios::app);</div><div class="line"><a name="l00483"></a><span class="lineno">  483</span>&#160;            out_file &lt;&lt; this-&gt;element_tag_[snapshot_index] &lt;&lt; <span class="stringliteral">&quot;   &quot;</span>;</div><div class="line"><a name="l00484"></a><span class="lineno">  484</span>&#160;            <span class="keywordflow">for</span> (<span class="keywordtype">int</span> observation_index = 0; observation_index != this-&gt;observation_; ++observation_index)</div><div class="line"><a name="l00485"></a><span class="lineno">  485</span>&#160;            {</div><div class="line"><a name="l00486"></a><span class="lineno">  486</span>&#160;                this-&gt;plt_engine_.writeAQuantity(out_file, this-&gt;current_result_[snapshot_index][observation_index]);</div><div class="line"><a name="l00487"></a><span class="lineno">  487</span>&#160;            }</div><div class="line"><a name="l00488"></a><span class="lineno">  488</span>&#160;            out_file &lt;&lt; <span class="stringliteral">&quot;\n&quot;</span>;</div><div class="line"><a name="l00489"></a><span class="lineno">  489</span>&#160;            out_file.close();</div><div class="line"><a name="l00490"></a><span class="lineno">  490</span>&#160;        }</div><div class="line"><a name="l00491"></a><span class="lineno">  491</span>&#160;    };</div><div class="line"><a name="l00492"></a><span class="lineno">  492</span>&#160;    <span class="comment">//=================================================================================================//</span></div><div class="line"><a name="l00493"></a><span class="lineno">  493</span>&#160;    <span class="keyword">template</span>&lt;<span class="keyword">class</span> ObserveMethodType&gt;</div><div class="line"><a name="l00494"></a><span class="lineno"><a class="line" href="class_s_p_h_1_1_regression_test_time_averaged.html#a87251561ae10d6858ab3982ff44bd937">  494</a></span>&#160;    <span class="keywordtype">void</span> <a class="code" href="class_s_p_h_1_1_regression_test_time_averaged.html#a87251561ae10d6858ab3982ff44bd937">RegressionTestTimeAveraged&lt;ObserveMethodType&gt;::updateMeanVariance</a>()</div><div class="line"><a name="l00495"></a><span class="lineno">  495</span>&#160;    {</div><div class="line"><a name="l00496"></a><span class="lineno">  496</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> observation_index = 0; observation_index != this-&gt;observation_; ++observation_index)</div><div class="line"><a name="l00497"></a><span class="lineno">  497</span>&#160;        {</div><div class="line"><a name="l00498"></a><span class="lineno">  498</span>&#160;            <span class="keywordflow">for</span> (<span class="keywordtype">int</span> snapshot_index = snapshot_for_converged_; snapshot_index != this-&gt;snapshot_; ++snapshot_index)</div><div class="line"><a name="l00499"></a><span class="lineno">  499</span>&#160;            {</div><div class="line"><a name="l00500"></a><span class="lineno">  500</span>&#160;                local_meanvalue_[observation_index] += this-&gt;current_result_[snapshot_index][observation_index];</div><div class="line"><a name="l00501"></a><span class="lineno">  501</span>&#160;            }</div><div class="line"><a name="l00502"></a><span class="lineno">  502</span>&#160;            local_meanvalue_[observation_index] = local_meanvalue_[observation_index] / (this-&gt;snapshot_ - snapshot_for_converged_);</div><div class="line"><a name="l00503"></a><span class="lineno">  503</span>&#160;            meanvalue_new_[observation_index] = (local_meanvalue_[observation_index] + meanvalue_[observation_index] * (this-&gt;number_of_run_ - 1)) / this-&gt;number_of_run_;</div><div class="line"><a name="l00504"></a><span class="lineno">  504</span>&#160;        }</div><div class="line"><a name="l00505"></a><span class="lineno">  505</span>&#160;        calculateNewVariance(this-&gt;current_result_trans_, local_meanvalue_, variance_, variance_new_);</div><div class="line"><a name="l00506"></a><span class="lineno">  506</span>&#160;    };</div><div class="line"><a name="l00507"></a><span class="lineno">  507</span>&#160;    <span class="comment">//=================================================================================================//</span></div><div class="line"><a name="l00508"></a><span class="lineno">  508</span>&#160;    <span class="keyword">template</span>&lt;<span class="keyword">class</span> ObserveMethodType&gt;</div><div class="line"><a name="l00509"></a><span class="lineno"><a class="line" href="class_s_p_h_1_1_regression_test_time_averaged.html#a98662f8e4692378cfc46932f13485cdc">  509</a></span>&#160;    <span class="keywordtype">void</span> <a class="code" href="class_s_p_h_1_1_regression_test_time_averaged.html#a98662f8e4692378cfc46932f13485cdc">RegressionTestTimeAveraged&lt;ObserveMethodType&gt;::writeMeanVarianceToXml</a>()</div><div class="line"><a name="l00510"></a><span class="lineno">  510</span>&#160;    {</div><div class="line"><a name="l00511"></a><span class="lineno">  511</span>&#160;        mean_variance_xml_engine_out_.addElementToXmlDoc(<span class="stringliteral">&quot;MeanValue_Element&quot;</span>);</div><div class="line"><a name="l00512"></a><span class="lineno">  512</span>&#160;        SimTK::Xml::Element meanvalue_element_ = mean_variance_xml_engine_out_.getChildElement(<span class="stringliteral">&quot;MeanValue_Element&quot;</span>);</div><div class="line"><a name="l00513"></a><span class="lineno">  513</span>&#160;        mean_variance_xml_engine_out_.addChildToElement(meanvalue_element_, <span class="stringliteral">&quot;Snapshot_MeanValue&quot;</span>);</div><div class="line"><a name="l00514"></a><span class="lineno">  514</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> observation_index = 0; observation_index != this-&gt;observation_; ++observation_index)</div><div class="line"><a name="l00515"></a><span class="lineno">  515</span>&#160;        {</div><div class="line"><a name="l00516"></a><span class="lineno">  516</span>&#160;            SimTK::Xml::element_iterator ele_ite_mean = meanvalue_element_.element_begin();</div><div class="line"><a name="l00517"></a><span class="lineno">  517</span>&#160;            std::string attribute_name_ = this-&gt;quantity_name_ + <span class="stringliteral">&quot;_&quot;</span> + std::to_string(observation_index);</div><div class="line"><a name="l00518"></a><span class="lineno">  518</span>&#160;            mean_variance_xml_engine_out_.setAttributeToElement(ele_ite_mean, attribute_name_, meanvalue_new_[observation_index]);</div><div class="line"><a name="l00519"></a><span class="lineno">  519</span>&#160;        }</div><div class="line"><a name="l00520"></a><span class="lineno">  520</span>&#160;        mean_variance_xml_engine_out_.addElementToXmlDoc(<span class="stringliteral">&quot;Variance_Element&quot;</span>);</div><div class="line"><a name="l00521"></a><span class="lineno">  521</span>&#160;        SimTK::Xml::Element variance_element_ = mean_variance_xml_engine_out_.getChildElement(<span class="stringliteral">&quot;Variance_Element&quot;</span>);</div><div class="line"><a name="l00522"></a><span class="lineno">  522</span>&#160;        mean_variance_xml_engine_out_.addChildToElement(variance_element_, <span class="stringliteral">&quot;Snapshot_Variance&quot;</span>);</div><div class="line"><a name="l00523"></a><span class="lineno">  523</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> observation_index = 0; observation_index != this-&gt;observation_; ++observation_index)</div><div class="line"><a name="l00524"></a><span class="lineno">  524</span>&#160;        {</div><div class="line"><a name="l00525"></a><span class="lineno">  525</span>&#160;            SimTK::Xml::element_iterator ele_ite_variance = variance_element_.element_begin();</div><div class="line"><a name="l00526"></a><span class="lineno">  526</span>&#160;            std::string attribute_name_ = this-&gt;quantity_name_ + <span class="stringliteral">&quot;_&quot;</span> + std::to_string(observation_index);</div><div class="line"><a name="l00527"></a><span class="lineno">  527</span>&#160;            mean_variance_xml_engine_out_.setAttributeToElement(ele_ite_variance, attribute_name_, variance_new_[observation_index]);</div><div class="line"><a name="l00528"></a><span class="lineno">  528</span>&#160;        }</div><div class="line"><a name="l00529"></a><span class="lineno">  529</span>&#160;        mean_variance_xml_engine_out_.writeToXmlFile(mean_variance_filefullpath_);</div><div class="line"><a name="l00530"></a><span class="lineno">  530</span>&#160;    };</div><div class="line"><a name="l00531"></a><span class="lineno">  531</span>&#160;    <span class="comment">//=================================================================================================//</span></div><div class="line"><a name="l00532"></a><span class="lineno">  532</span>&#160;    <span class="keyword">template</span>&lt;<span class="keyword">class</span> ObserveMethodType&gt;</div><div class="line"><a name="l00533"></a><span class="lineno"><a class="line" href="class_s_p_h_1_1_regression_test_time_averaged.html#a44a2a1a9d720699f0b30d9f59c4a7947">  533</a></span>&#160;    <span class="keywordtype">bool</span> <a class="code" href="class_s_p_h_1_1_regression_test_time_averaged.html#a44a2a1a9d720699f0b30d9f59c4a7947">RegressionTestTimeAveraged&lt;ObserveMethodType&gt;::compareMeanVariance</a>()</div><div class="line"><a name="l00534"></a><span class="lineno">  534</span>&#160;    {</div><div class="line"><a name="l00535"></a><span class="lineno">  535</span>&#160;        <span class="keywordtype">int</span> count_not_converged_m = 0;</div><div class="line"><a name="l00536"></a><span class="lineno">  536</span>&#160;        <span class="keywordtype">int</span> count_not_converged_v = 0;</div><div class="line"><a name="l00537"></a><span class="lineno">  537</span>&#160;        count_not_converged_m = this-&gt;compareParameter(<span class="stringliteral">&quot;meanvalue&quot;</span>, this-&gt;meanvalue_, this-&gt;meanvalue_new_, this-&gt;threshold_mean_);</div><div class="line"><a name="l00538"></a><span class="lineno">  538</span>&#160;        count_not_converged_v = this-&gt;compareParameter(<span class="stringliteral">&quot;variance&quot;</span>, this-&gt;variance_, this-&gt;variance_new_, this-&gt;threshold_variance_);</div><div class="line"><a name="l00539"></a><span class="lineno">  539</span>&#160;        <span class="keywordflow">if</span> (count_not_converged_m == 0)</div><div class="line"><a name="l00540"></a><span class="lineno">  540</span>&#160;        {</div><div class="line"><a name="l00541"></a><span class="lineno">  541</span>&#160;            std::cout &lt;&lt; <span class="stringliteral">&quot;The meanvalue of &quot;</span> &lt;&lt; this-&gt;quantity_name_ &lt;&lt; <span class="stringliteral">&quot; are converged now.&quot;</span> &lt;&lt; endl;</div><div class="line"><a name="l00542"></a><span class="lineno">  542</span>&#160;            <span class="keywordflow">if</span> (count_not_converged_v == 0)</div><div class="line"><a name="l00543"></a><span class="lineno">  543</span>&#160;            {</div><div class="line"><a name="l00544"></a><span class="lineno">  544</span>&#160;                <span class="keywordflow">if</span> (this-&gt;label_for_repeat_ == 4)</div><div class="line"><a name="l00545"></a><span class="lineno">  545</span>&#160;                {</div><div class="line"><a name="l00546"></a><span class="lineno">  546</span>&#160;                    this-&gt;converged = <span class="stringliteral">&quot;true&quot;</span>;</div><div class="line"><a name="l00547"></a><span class="lineno">  547</span>&#160;                    this-&gt;label_for_repeat_++;</div><div class="line"><a name="l00548"></a><span class="lineno">  548</span>&#160;                    std::cout &lt;&lt; <span class="stringliteral">&quot;The meanvalue and variance of &quot;</span> &lt;&lt; this-&gt;quantity_name_ &lt;&lt; <span class="stringliteral">&quot; are converged enough times, and run will stop now.&quot;</span> &lt;&lt; endl;</div><div class="line"><a name="l00549"></a><span class="lineno">  549</span>&#160;                    <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00550"></a><span class="lineno">  550</span>&#160;                }</div><div class="line"><a name="l00551"></a><span class="lineno">  551</span>&#160;                <span class="keywordflow">else</span></div><div class="line"><a name="l00552"></a><span class="lineno">  552</span>&#160;                {</div><div class="line"><a name="l00553"></a><span class="lineno">  553</span>&#160;                    this-&gt;converged = <span class="stringliteral">&quot;false&quot;</span>;</div><div class="line"><a name="l00554"></a><span class="lineno">  554</span>&#160;                    this-&gt;label_for_repeat_++;</div><div class="line"><a name="l00555"></a><span class="lineno">  555</span>&#160;                    std::cout &lt;&lt; <span class="stringliteral">&quot;The variance of &quot;</span> &lt;&lt; this-&gt;quantity_name_ &lt;&lt; <span class="stringliteral">&quot; are also converged, and this is the &quot;</span> &lt;&lt; this-&gt;label_for_repeat_</div><div class="line"><a name="l00556"></a><span class="lineno">  556</span>&#160;                        &lt;&lt; <span class="stringliteral">&quot; times. They should be converged more times to be stable.&quot;</span> &lt;&lt; endl;</div><div class="line"><a name="l00557"></a><span class="lineno">  557</span>&#160;                    <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00558"></a><span class="lineno">  558</span>&#160;                }</div><div class="line"><a name="l00559"></a><span class="lineno">  559</span>&#160;            }</div><div class="line"><a name="l00560"></a><span class="lineno">  560</span>&#160;            <span class="keywordflow">else</span> <span class="keywordflow">if</span> (count_not_converged_v != 0)</div><div class="line"><a name="l00561"></a><span class="lineno">  561</span>&#160;            {</div><div class="line"><a name="l00562"></a><span class="lineno">  562</span>&#160;                this-&gt;converged = <span class="stringliteral">&quot;false&quot;</span>;</div><div class="line"><a name="l00563"></a><span class="lineno">  563</span>&#160;                this-&gt;label_for_repeat_ = 0;</div><div class="line"><a name="l00564"></a><span class="lineno">  564</span>&#160;                std::cout &lt;&lt; <span class="stringliteral">&quot;The variance of &quot;</span> &lt;&lt; this-&gt;quantity_name_ &lt;&lt; <span class="stringliteral">&quot; are not converged &quot;</span> &lt;&lt; count_not_converged_v &lt;&lt; <span class="stringliteral">&quot; times.&quot;</span> &lt;&lt; endl;</div><div class="line"><a name="l00565"></a><span class="lineno">  565</span>&#160;                <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00566"></a><span class="lineno">  566</span>&#160;            };</div><div class="line"><a name="l00567"></a><span class="lineno">  567</span>&#160;        }</div><div class="line"><a name="l00568"></a><span class="lineno">  568</span>&#160;        <span class="keywordflow">else</span> <span class="keywordflow">if</span> (count_not_converged_m != 0)</div><div class="line"><a name="l00569"></a><span class="lineno">  569</span>&#160;        {</div><div class="line"><a name="l00570"></a><span class="lineno">  570</span>&#160;            this-&gt;converged = <span class="stringliteral">&quot;false&quot;</span>;</div><div class="line"><a name="l00571"></a><span class="lineno">  571</span>&#160;            this-&gt;label_for_repeat_ = 0;</div><div class="line"><a name="l00572"></a><span class="lineno">  572</span>&#160;            std::cout &lt;&lt; <span class="stringliteral">&quot;The meanvalue of &quot;</span> &lt;&lt; this-&gt;quantity_name_ &lt;&lt; <span class="stringliteral">&quot; are not converged &quot;</span> &lt;&lt; count_not_converged_m &lt;&lt; <span class="stringliteral">&quot; times.&quot;</span> &lt;&lt; endl;</div><div class="line"><a name="l00573"></a><span class="lineno">  573</span>&#160;            <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00574"></a><span class="lineno">  574</span>&#160;        }</div><div class="line"><a name="l00575"></a><span class="lineno">  575</span>&#160;    };</div><div class="line"><a name="l00576"></a><span class="lineno">  576</span>&#160;    <span class="comment">//=================================================================================================//   </span></div><div class="line"><a name="l00577"></a><span class="lineno">  577</span>&#160;    <span class="keyword">template</span>&lt;<span class="keyword">class</span> ObserveMethodType&gt;</div><div class="line"><a name="l00578"></a><span class="lineno"><a class="line" href="class_s_p_h_1_1_regression_test_time_averaged.html#a173d112f4b9abd96ccb113e76dff887a">  578</a></span>&#160;    <span class="keywordtype">void</span> <a class="code" href="class_s_p_h_1_1_regression_test_time_averaged.html#a173d112f4b9abd96ccb113e76dff887a">RegressionTestTimeAveraged&lt;ObserveMethodType&gt;::resultTest</a>()</div><div class="line"><a name="l00579"></a><span class="lineno">  579</span>&#160;    {</div><div class="line"><a name="l00580"></a><span class="lineno">  580</span>&#160;        <span class="keywordtype">int</span> test_wrong = 0;</div><div class="line"><a name="l00581"></a><span class="lineno">  581</span>&#160;        </div><div class="line"><a name="l00582"></a><span class="lineno">  582</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> observation_index = 0; observation_index != this-&gt;observation_; ++observation_index)</div><div class="line"><a name="l00583"></a><span class="lineno">  583</span>&#160;        {</div><div class="line"><a name="l00584"></a><span class="lineno">  584</span>&#160;            <span class="keywordflow">for</span> (<span class="keywordtype">int</span> snapshot_index = snapshot_for_converged_; snapshot_index != this-&gt;snapshot_; ++snapshot_index)</div><div class="line"><a name="l00585"></a><span class="lineno">  585</span>&#160;                local_meanvalue_[observation_index] += this-&gt;current_result_[snapshot_index][observation_index];</div><div class="line"><a name="l00586"></a><span class="lineno">  586</span>&#160;            local_meanvalue_[observation_index] = local_meanvalue_[observation_index] / (this-&gt;snapshot_-snapshot_for_converged_);</div><div class="line"><a name="l00587"></a><span class="lineno">  587</span>&#160;        }</div><div class="line"><a name="l00588"></a><span class="lineno">  588</span>&#160;</div><div class="line"><a name="l00589"></a><span class="lineno">  589</span>&#160;        test_wrong = testNewResult(this-&gt;current_result_, meanvalue_, local_meanvalue_, variance_);</div><div class="line"><a name="l00590"></a><span class="lineno">  590</span>&#160;        <span class="keywordflow">if</span> (test_wrong == 0)</div><div class="line"><a name="l00591"></a><span class="lineno">  591</span>&#160;            std::cout &lt;&lt; <span class="stringliteral">&quot;The result of &quot;</span> &lt;&lt; this-&gt;quantity_name_ &lt;&lt; <span class="stringliteral">&quot; is correct based on the time-averaged regression test!&quot;</span> &lt;&lt; endl;</div><div class="line"><a name="l00592"></a><span class="lineno">  592</span>&#160;        <span class="keywordflow">else</span></div><div class="line"><a name="l00593"></a><span class="lineno">  593</span>&#160;        {</div><div class="line"><a name="l00594"></a><span class="lineno">  594</span>&#160;            std::cout &lt;&lt; <span class="stringliteral">&quot;There are &quot;</span> &lt;&lt; test_wrong &lt;&lt; <span class="stringliteral">&quot; particles are not within the expected range.&quot;</span> &lt;&lt; endl;</div><div class="line"><a name="l00595"></a><span class="lineno">  595</span>&#160;            std::cout &lt;&lt; <span class="stringliteral">&quot;Please try again. If it still post this conclusion, the result is not correct!&quot;</span> &lt;&lt; endl;</div><div class="line"><a name="l00596"></a><span class="lineno">  596</span>&#160;            exit(1);</div><div class="line"><a name="l00597"></a><span class="lineno">  597</span>&#160;        }</div><div class="line"><a name="l00598"></a><span class="lineno">  598</span>&#160;    };</div><div class="line"><a name="l00599"></a><span class="lineno">  599</span>&#160;    <span class="comment">//=================================================================================================//</span></div><div class="line"><a name="l00600"></a><span class="lineno">  600</span>&#160;};</div><div class="ttc" id="class_s_p_h_1_1_regression_test_time_averaged_html_aa72b7029d93a690ff6699be64c76d3b9"><div class="ttname"><a href="class_s_p_h_1_1_regression_test_time_averaged.html#aa72b7029d93a690ff6699be64c76d3b9">SPH::RegressionTestTimeAveraged::testNewResult</a></div><div class="ttdeci">int testNewResult(DoubleVec&lt; Real &gt; &amp;current_result, StdVec&lt; Real &gt; &amp;meanvalue, StdVec&lt; Real &gt; &amp;local_meanvalue, StdVec&lt; Real &gt; &amp;variance)</div><div class="ttdef"><b>Definition:</b> time_averaged_method.hpp:305</div></div>
<div class="ttc" id="class_s_p_h_1_1_regression_test_time_averaged_html_a87251561ae10d6858ab3982ff44bd937"><div class="ttname"><a href="class_s_p_h_1_1_regression_test_time_averaged.html#a87251561ae10d6858ab3982ff44bd937">SPH::RegressionTestTimeAveraged::updateMeanVariance</a></div><div class="ttdeci">void updateMeanVariance()</div><div class="ttdef"><b>Definition:</b> time_averaged_method.hpp:494</div></div>
<div class="ttc" id="class_s_p_h_1_1_regression_test_time_averaged_html_a03c15934c73e44ef45c11475c3245d71"><div class="ttname"><a href="class_s_p_h_1_1_regression_test_time_averaged.html#a03c15934c73e44ef45c11475c3245d71">SPH::RegressionTestTimeAveraged::compareParameter</a></div><div class="ttdeci">int compareParameter(string par_name, StdVec&lt; Real &gt; &amp;parameter, StdVec&lt; Real &gt; &amp;parameter_new, Real &amp;threshold)</div><div class="ttdef"><b>Definition:</b> time_averaged_method.hpp:230</div></div>
<div class="ttc" id="class_s_p_h_1_1_regression_test_time_averaged_html_a400f76d3203ec1f7d6c1fd4caa1329de"><div class="ttname"><a href="class_s_p_h_1_1_regression_test_time_averaged.html#a400f76d3203ec1f7d6c1fd4caa1329de">SPH::RegressionTestTimeAveraged::filterLocalResult</a></div><div class="ttdeci">void filterLocalResult(DoubleVec&lt; Real &gt; &amp;current_result)</div><div class="ttdef"><b>Definition:</b> time_averaged_method.hpp:15</div></div>
<div class="ttc" id="class_s_p_h_1_1_regression_test_time_averaged_html_a1b4e1fb0a6f9454834003c3f246a2fac"><div class="ttname"><a href="class_s_p_h_1_1_regression_test_time_averaged.html#a1b4e1fb0a6f9454834003c3f246a2fac">SPH::RegressionTestTimeAveraged::filterExtremeValues</a></div><div class="ttdeci">void filterExtremeValues()</div><div class="ttdef"><b>Definition:</b> time_averaged_method.hpp:465</div></div>
<div class="ttc" id="class_s_p_h_1_1_regression_test_time_averaged_html_a98662f8e4692378cfc46932f13485cdc"><div class="ttname"><a href="class_s_p_h_1_1_regression_test_time_averaged.html#a98662f8e4692378cfc46932f13485cdc">SPH::RegressionTestTimeAveraged::writeMeanVarianceToXml</a></div><div class="ttdeci">void writeMeanVarianceToXml()</div><div class="ttdef"><b>Definition:</b> time_averaged_method.hpp:509</div></div>
<div class="ttc" id="class_s_p_h_1_1_regression_test_time_averaged_html_a622f8618bab75f36e56a713bf4e89615"><div class="ttname"><a href="class_s_p_h_1_1_regression_test_time_averaged.html#a622f8618bab75f36e56a713bf4e89615">SPH::RegressionTestTimeAveraged::initializeThreshold</a></div><div class="ttdeci">void initializeThreshold(VariableType &amp;threshold_mean, VariableType &amp;threshold_variance)</div><div class="ttdef"><b>Definition:</b> time_averaged_method.hpp:404</div></div>
<div class="ttc" id="class_s_p_h_1_1_regression_test_time_averaged_html_a173d112f4b9abd96ccb113e76dff887a"><div class="ttname"><a href="class_s_p_h_1_1_regression_test_time_averaged.html#a173d112f4b9abd96ccb113e76dff887a">SPH::RegressionTestTimeAveraged::resultTest</a></div><div class="ttdeci">void resultTest()</div><div class="ttdef"><b>Definition:</b> time_averaged_method.hpp:578</div></div>
<div class="ttc" id="class_s_p_h_1_1_regression_test_time_averaged_html_aea0ca51a8c49bd413314ff5fbfff5904"><div class="ttname"><a href="class_s_p_h_1_1_regression_test_time_averaged.html#aea0ca51a8c49bd413314ff5fbfff5904">SPH::RegressionTestTimeAveraged::searchForStartPoint</a></div><div class="ttdeci">void searchForStartPoint()</div><div class="ttdef"><b>Definition:</b> time_averaged_method.hpp:457</div></div>
<div class="ttc" id="class_s_p_h_1_1_regression_test_time_averaged_html_a44a2a1a9d720699f0b30d9f59c4a7947"><div class="ttname"><a href="class_s_p_h_1_1_regression_test_time_averaged.html#a44a2a1a9d720699f0b30d9f59c4a7947">SPH::RegressionTestTimeAveraged::compareMeanVariance</a></div><div class="ttdeci">bool compareMeanVariance()</div><div class="ttdef"><b>Definition:</b> time_averaged_method.hpp:533</div></div>
<div class="ttc" id="class_std_vec_3_01_real_01_4_html"><div class="ttname"><a href="class_std_vec_3_01_real_01_4.html">StdVec&lt; Real &gt;</a></div></div>
<div class="ttc" id="class_s_p_h_1_1_regression_test_time_averaged_html_a9e897ed946fa84afe6c2624a5b46f80b"><div class="ttname"><a href="class_s_p_h_1_1_regression_test_time_averaged.html#a9e897ed946fa84afe6c2624a5b46f80b">SPH::RegressionTestTimeAveraged::calculateNewVariance</a></div><div class="ttdeci">void calculateNewVariance(DoubleVec&lt; Real &gt; &amp;current_result, StdVec&lt; Real &gt; &amp;local_meanvalue, StdVec&lt; Real &gt; &amp;variance, StdVec&lt; Real &gt; &amp;variance_new)</div><div class="ttdef"><b>Definition:</b> time_averaged_method.hpp:191</div></div>
<div class="ttc" id="class_s_p_h_1_1_regression_test_time_averaged_html_a6a84508bedab4d15b399451f2a37462b"><div class="ttname"><a href="class_s_p_h_1_1_regression_test_time_averaged.html#a6a84508bedab4d15b399451f2a37462b">SPH::RegressionTestTimeAveraged::readMeanVarianceFromXml</a></div><div class="ttdeci">void readMeanVarianceFromXml()</div><div class="ttdef"><b>Definition:</b> time_averaged_method.hpp:431</div></div>
<div class="ttc" id="time__averaged__method_8h_html"><div class="ttname"><a href="time__averaged__method_8h.html">time_averaged_method.h</a></div><div class="ttdoc">Classes for the comparison between validated and tested results with time-averaged meanvalue and vari...</div></div>
<div class="ttc" id="class_s_p_h_1_1_regression_test_time_averaged_html_a15897128c44c33ecffc2bec85fb4ccc3"><div class="ttname"><a href="class_s_p_h_1_1_regression_test_time_averaged.html#a15897128c44c33ecffc2bec85fb4ccc3">SPH::RegressionTestTimeAveraged::searchSteadyStart</a></div><div class="ttdeci">void searchSteadyStart(DoubleVec&lt; Real &gt; &amp;current_result)</div><div class="ttdef"><b>Definition:</b> time_averaged_method.hpp:120</div></div>
<div class="ttc" id="namespace_s_p_h_html"><div class="ttname"><a href="namespace_s_p_h.html">SPH</a></div><div class="ttdef"><b>Definition:</b> solid_body_supplementary.cpp:9</div></div>
<div class="ttc" id="class_s_p_h_1_1_regression_test_time_averaged_html"><div class="ttname"><a href="class_s_p_h_1_1_regression_test_time_averaged.html">SPH::RegressionTestTimeAveraged</a></div><div class="ttdoc">The regression test is based on the time-averaged meanvalue and variance. </div><div class="ttdef"><b>Definition:</b> time_averaged_method.h:40</div></div>
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